class_docstr: DJL build logic for ModelBuilder()
code: def __init__(self):
      self.model = None
      self.serve_settings = None
      self.sagemaker_session = None
      self.model_path = None
      self.dependencies = None
      self.modes = None
      self.mode = None
      self.model_server = None
      self.image_uri = None
      self._original_deploy = None
      self.secret_key = None
      self.engine = None
      self.hf_model_config = None
      self._default_tensor_parallel_degree = None
      self._default_data_type = None
      self._default_max_tokens = None
      self._default_max_new_tokens = None
      self.pysdk_model = None
      self.overwrite_props_from_file = None
      self.schema_builder = None
      self.env_vars = None
      self.nb_instance_type = None
      self.ram_usage_model_load = None
  
imports: from __future__ import absolute_import
  import logging
  from typing import Type
  from abc import ABC, abstractmethod
  from pathlib import Path
  from datetime import datetime, timedelta
  from sagemaker.model import Model
  from sagemaker.serve.utils.exceptions import LocalDeepPingException, LocalModelLoadException, LocalModelOutOfMemoryException, LocalModelInvocationException
  from sagemaker.serve.utils.tuning import _serial_benchmark, _concurrent_benchmark, _more_performant, _pretty_print_results
  from sagemaker.serve.model_server.djl_serving.utils import _auto_detect_engine, _set_serve_properties, _get_admissible_tensor_parallel_degrees, _get_admissible_dtypes, _get_default_tensor_parallel_degree
  from sagemaker.serve.utils.local_hardware import _get_nb_instance, _get_ram_usage_mb, _get_gpu_info, _get_gpu_info_fallback
  from sagemaker.serve.model_server.djl_serving.prepare import prepare_for_djl_serving, _create_dir_structure
  from sagemaker.serve.utils.predictors import DjlLocalModePredictor
  from sagemaker.serve.utils.types import ModelServer, _DjlEngine
  from sagemaker.serve.mode.function_pointers import Mode
  from sagemaker.serve.utils.telemetry_logger import _capture_telemetry
  from sagemaker.djl_inference.model import DeepSpeedModel, FasterTransformerModel, HuggingFaceAccelerateModel
  from sagemaker.base_predictor import PredictorBase
label_desc: Use image node_id 1 to create a new DJL object from inherited base classes: ABC with example: obj = DJL()